Housekeeping

Loading in data

OTU

ANALYSIS

OTU diversity and such

OTU_table.t<-transpose.OTU(OTU_table)
ab<-table(unlist(OTU_table.t))
ab[1:20] 

barplot(log(ab), las=1, xlab = "Abundand in matrix", 
        ylab = "log (frequency)", 
        ylim = c(0, max(log(ab))+2))


row.names(OTU_table)<-OTU_table$OTU_ID
OTU_table.OTU_ID<-OTU_table$OTU_ID
OTU_table$OTU_ID<-NULL
OTU_table<-OTU_table[,colSums(OTU_table[,c(1:(length(colnames(OTU_table))-1))])!=0]
OTU_table.t<-transpose.OTU(OTU_table)


OTU_table.ns<-OTU_table[rowSums(OTU_table[,c(1:(length(OTU_table)-1))])!=1,]
dim(OTU_table)
dim(OTU_table.ns)


OTU_table<-OTU_table.ns
OTU_table.t<-transpose.OTU(OTU_table)

# Seperate taxonomy
OTU_table$taxonomy<-as.character(OTU_table$taxonomy)

tax.classes <- c("kingdom", "phylum", "class", "order", "family", "genus", "species")
OTU_table.tax <- col.splitup(OTU_table, col="taxonomy", sep="; ",drop=TRUE, names=tax.classes)
OTU_table.tax$taxonomy<-OTU_table$taxonomy

Making the metadata

Diversity Model

# Normal?
colnames(OTU.info2)
colnames(OTU.info2)[colnames(OTU.info2)=="Annual.mean.temperature...C."] <- "AnnualMeanTemperature"
colnames(OTU.info2)[colnames(OTU.info2)=="Mean.diurnal.temperature.range..mean.period.max.min.....C."] <- "MeanDiurnalTemperatureRange"
colnames(OTU.info2)[colnames(OTU.info2)=="Temperature.seasonality..C.of.V."] <- "TemperatureSeasonality"
colnames(OTU.info2)[colnames(OTU.info2)=="Max.temperature.of.warmest.week...C."] <- "MaxTemperature"
colnames(OTU.info2)[colnames(OTU.info2)=="Min.temperature.of.coldest.week...C."] <- "MinTemperature"
colnames(OTU.info2)[colnames(OTU.info2)=="Annual.precipitation..mm."] <- "AnnualPrecipitation"
colnames(OTU.info2)[colnames(OTU.info2)=="Precipitation.of.wettest.week..mm."] <- "MaxPrecipitation"
colnames(OTU.info2)[colnames(OTU.info2)=="Precipitation.of.driest.quarter..mm."] <- "MinPrecipitation"
colnames(OTU.info2)[colnames(OTU.info2)=="Precipitation.of.coldest.quarter..mm."] <- "ColdestPrecipitation"
colnames(OTU.info2)[colnames(OTU.info2)=="Precipitation.seasonality..C.of.V."] <- "PrecipitationSeasonality"


Mydata<-OTU.info2[,c(1:6,8:10,16:17,19,21,23)]
Y<-Mydata[,c(8:length(Mydata))]
covar_cor = cor(Y)
corrplot.mixed(covar_cor, upper = "ellipse", lower = "number")
Mydata_PCA = dudi.pca(df = Y, scannf = FALSE, nf = 2, center = TRUE, scale = TRUE)
Mydata_PCA$co
s.corcircle(Mydata_PCA$co)
Mydata_PCA$eig/sum(Mydata_PCA$eig)
colnames(Mydata)

Mydata$Lat <- scale(Mydata$Lat)
Mydata$Long <- scale(Mydata$Long)
Mydata$AnnualPrecipitation <- scale(Mydata$AnnualPrecipitation)
Mydata$AnnualMeanTemperature <- scale(Mydata$AnnualMeanTemperature)
Mydata$PrecipitationSeasonality <- scale(Mydata$PrecipitationSeasonality)
Mydata$TemperatureSeasonality  <- scale(Mydata$TemperatureSeasonality)
Mydata$MeanDiurnalTemperatureRange<- scale(Mydata$MeanDiurnalTemperatureRange)
Mydata$MinTemperature <- scale(Mydata$MinTemperature)
Mydata$MaxTemperature <- scale(Mydata$MaxTemperature)



mod1<-lm(mean.Alpha.div ~ Lat + Long + AnnualPrecipitation + AnnualMeanTemperature + PrecipitationSeasonality + TemperatureSeasonality  + MeanDiurnalTemperatureRange+ MinTemperature + MaxTemperature , data = Mydata)

car::qqp(OTU.info2$mean.Alpha.div, "norm")
plot(density(OTU.info2$mean.Alpha.div))
shapiro.test(OTU.info2$mean.Alpha.div)
bptest(mod1)
plot(mod1)

# Is endophyte diversity driven by any climatic data? NO
mod1.mLat<-update(mod1,.~. - Lat)
anova(mod1,mod1.mLat)

mod1.mLong<-update(mod1.mLat,.~. - Long)
anova(mod1.mLat,mod1.mLong)

mod1.mAnnualPrecipitation<-update(mod1.mLong,.~. - AnnualPrecipitation)
anova(mod1.mLong,mod1.mAnnualPrecipitation)

mod1.mAnnualMeanTemperature<-update(mod1.mAnnualPrecipitation,.~. - AnnualMeanTemperature)
anova(mod1.mAnnualPrecipitation,mod1.mAnnualMeanTemperature)

mod1.mPrecipitationSeasonality<-update(mod1.mAnnualMeanTemperature,.~. - PrecipitationSeasonality)
anova(mod1.mAnnualMeanTemperature, mod1.mPrecipitationSeasonality)

mod1.mTemperatureSeasonality<-update(mod1.mPrecipitationSeasonality,.~. - TemperatureSeasonality)
anova(mod1.mPrecipitationSeasonality,mod1.mTemperatureSeasonality)

mod1.mMeanDiurnalTemperatureRange<-update(mod1.mTemperatureSeasonality,.~. - MeanDiurnalTemperatureRange)
anova(mod1.mTemperatureSeasonality, mod1.mMeanDiurnalTemperatureRange)

# Min temperature is a significant predictor!
mod1.mMinTemperature<-update(mod1.mMeanDiurnalTemperatureRange,.~. - MinTemperature)
anova(mod1.mMeanDiurnalTemperatureRange, mod1.mMinTemperature)

# Max temperature is a significant predictor!
mod1.mMaxTemperature<-update(mod1.mMinTemperature,.~. - MaxTemperature)
anova(mod1.mMinTemperature, mod1.mMaxTemperature)


mod2<- lm(Alpha.div~Species,data=OTU.info)
summary(mod2)

Composition model

FIGURES

OTU.info$Species<-plyr::revalue(OTU.info$Species, c("Brassica_napus"="Brassica napus", "Brassica_oleracea"="Brassica oleracea","Cratoneuron"="Cratoneuron filicinum","Grimmia"="Grimmia pilifera","Microdesmis"="Microdesmis caseariifolia","Orchid"="Phalaenopsis","Oryza"="Oryza sativa","Paeonia_rockii"="Paeonia rockii","Paeonia_suffruticosa"="Paeonia suffruticosa","Pinus"="Pinus pinaster","Pinus_flexilis"="Pinus flexilis","Pylaisiella"="Pylaisia polyantha","Rothmannia"="Rothmannia macrophylla","Santiria"="Santiria apiculata","Solanum"="Solanum lycopersicum","Vitis"="Vitis vinifera","Zea"="Zea mays"))


# Alpha to secies
Adiv_species_OTU<-ggplot(OTU.info, aes(y=Alpha.div,x=Species))+
  geom_boxplot(aes(color=Species))+
  labs(y="Shannon diversity")+
  theme_katherine()+ theme(axis.text.x = element_text(angle = 90, hjust = 1),legend.position="none")+
  ggtitle("Alpha Diversity of OTUs by Species")
ggsave("Adiv_species.OTU_PLOT.pdf",Adiv_species_OTU)

# RDA
smry <- summary(formulaRDA)
df1  <- data.frame(smry$sites[,1:2])       # PC1 and PC2
df2  <- data.frame(smry$biplot[,1:2])     # loadings for PC1 and PC2
rda.plot <- ggplot(df1, aes(x=RDA1, y=RDA2)) + 
  geom_text(aes(label=rownames(df1)),size=3,position=position_jitter(width=.2,height=.2)) +
  geom_point(aes(alpha=0.3)) +
  geom_hline(yintercept=0, linetype="dotted") +
  geom_vline(xintercept=0, linetype="dotted")  

names<-c("Latitude","Longitude","\nMean Annual Temp", "Diurnal Temp Range", "\nTemp Seasonality", "Max Temp\n", "Min Temp", "Annual Precipitation\n", "Max Precipitation", "Precipitation Seasonality", "Min Precipitation", "Coldest Precipitation    ")
rownames(df2)
(formulaRDA.PLOT<-rda.plot +
  geom_segment(data=df2, aes(x=0, xend=RDA1, y=0, yend=RDA2), 
               color="red", arrow=arrow(length=unit(0.01,"npc"))) +
  geom_text(data=df2, 
            aes(x=RDA1,y=RDA2,label=names,
                hjust=0.5*(1-sign(RDA1)),vjust=0.5*(1-sign(RDA2))), 
            color="red", size=4)+
  coord_cartesian(ylim = c(-0.8, 0.8),xlim = c(-1.3, 1.3)) +theme_katherine()+ theme(legend.position="none"))
ggsave("formulaRDA.OTU_PLOT.pdf",formulaRDA.PLOT)

# NMDS
#Make a matrix with no row or column equal to 0 (do not enclude the env variable (GM COVERAGE))
OTU_table.t2<-merge(OTU_table.t,X, by="SampleID")
OTU_table.t2$SampleID<-NULL
rownames.M<-OTU_table.t2$Species
OTU_table.t2$Species<-NULL
M <- as.matrix(OTU_table.t2)
M[is.na(M)] <- 0
which( colSums(M)==0 )
which( rowSums(M)==0 )

rownames(M) <- rownames.M

dist_M <- vegdist(M, method = "bray", binary = T)
#The metaMDS analysis could have done the distance matrix internally but i would rather control it since i have presence/abscence
meta.nmds <- metaMDS(dist_M, k=2, trymax = 2000)
str(meta.nmds)
stressplot(meta.nmds)

Species<-data.frame(Species=as.factor(rownames.M))
# envfit
envfit <- envfit(meta.nmds, env = Species, perm = 999) #standard envfit
envfit

#data for plotting 
##NMDS points
NMDS.data<-Species 
NMDS.data$NMDS1<-meta.nmds$points[ ,1] 
NMDS.data$NMDS2<-meta.nmds$points[ ,2] 
colnames(NMDS.data)[1] <- "Species"

mult <- 1 #multiplier for the arrows and text for envfit below. You can change this and then rerun the plot command.
library(ggplot2)
(OTU.nmds.gg1 <- ggplot(data = NMDS.data, aes(y = NMDS2, x = NMDS1))+ 
    geom_point( aes(color = NMDS.data$Species), size = 1.5,alpha=0.6)+ 
    coord_cartesian(xlim = c(-0.25,0.25),ylim = c(-0.1,0.1)) + 
    theme_katherine())
ggsave("nmds.OTU_PLOT.pdf",OTU.nmds.gg1)

Functions

ANALYSIS

Functional diversity and such

metagenome_predictions
metagenome_predictions<-metagenome_predictions[,colSums(metagenome_predictions[,])!=0]
metagenome_predictions<-metagenome_predictions[rowSums(metagenome_predictions[,])!=0,]
metagenome_predictions.ns<-metagenome_predictions[,colSums(metagenome_predictions[,])!=1]
dim(metagenome_predictions)
[1]  177 3424
dim(metagenome_predictions.ns)
[1]  177 3407
metagenome_predictions<-metagenome_predictions.ns
Functions<-colnames(metagenome_predictions)
Samples<-rownames(metagenome_predictions)
metagenome_predictions.info<-data.frame(SampleID=Samples, OTU.Alpha.div=rep(NA, length(Samples)),Species=rep(NA, length(Samples)), Function.Alpha.div=rep(NA, length(Samples)))
which(sapply(OTU_table.t, is.character))
named integer(0)
OTU_table.t$SampleID<-NULL
for(i in 1:(length(Samples))){
metagenome_predictions.info$SampleID[i]<-Samples[i]
metagenome_predictions.info$OTU.Alpha.div[i]<-diversity(OTU_table.t[rownames(OTU_table.t)==Samples[i],], index = "shannon", MARGIN = 1, base = exp(1))
metagenome_predictions.info$Function.Alpha.div[i]<-diversity(metagenome_predictions[rownames(metagenome_predictions)==Samples[i],], index = "shannon", MARGIN = 1, base = exp(1))
metagenome_predictions.info$Species[i]<-mapping_file[mapping_file$SampleID==Samples[i],]$Species
}
metagenome_predictions.info$Species<-as.factor(metagenome_predictions.info$Species)
metagenome_predictions.info %>% dplyr::group_by(Species) %>% summarise(mean.OTU.Alpha.div=mean(OTU.Alpha.div),mean.Function.Alpha.div=mean(Function.Alpha.div)) -> metagenome_predictions.info2
metagenome_predictions.info2<-merge(metagenome_predictions.info2, BioDatFull2, by="Species")

Functional diversity model

# Normal?
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Annual.mean.temperature...C."] <- "AnnualMeanTemperature"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Mean.diurnal.temperature.range..mean.period.max.min.....C."] <- "MeanDiurnalTemperatureRange"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Temperature.seasonality..C.of.V."] <- "TemperatureSeasonality"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Max.temperature.of.warmest.week...C."] <- "MaxTemperature"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Min.temperature.of.coldest.week...C."] <- "MinTemperature"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Annual.precipitation..mm."] <- "AnnualPrecipitation"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Precipitation.of.wettest.week..mm."] <- "MaxPrecipitation"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Precipitation.of.driest.quarter..mm."] <- "MinPrecipitation"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Precipitation.of.coldest.quarter..mm."] <- "ColdestPrecipitation"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Precipitation.seasonality..C.of.V."] <- "PrecipitationSeasonality"


Mydata<-metagenome_predictions.info2[,c(1:7,9:11,17,18,20,22,24)]
colnames(metagenome_predictions.info2)
Mydata$Lat <- scale(Mydata$Lat)
Mydata$Long <- scale(Mydata$Long)
Mydata$AnnualPrecipitation <- scale(Mydata$AnnualPrecipitation)
Mydata$AnnualMeanTemperature <- scale(Mydata$AnnualMeanTemperature)
Mydata$PrecipitationSeasonality <- scale(Mydata$PrecipitationSeasonality)
Mydata$TemperatureSeasonality  <- scale(Mydata$TemperatureSeasonality)
Mydata$MeanDiurnalTemperatureRange<- scale(Mydata$MeanDiurnalTemperatureRange)
Mydata$MinTemperature <- scale(Mydata$MinTemperature)
Mydata$MaxTemperature <- scale(Mydata$MaxTemperature)



mod3<-lm(mean.Function.Alpha.div ~ Lat + Long + AnnualPrecipitation + AnnualMeanTemperature + PrecipitationSeasonality + TemperatureSeasonality  + MeanDiurnalTemperatureRange+ MinTemperature + MaxTemperature , data = Mydata)

car::qqp(metagenome_predictions.info2$mean.Function.Alpha.div, "norm")
plot(density(metagenome_predictions.info2$mean.Function.Alpha.div))
shapiro.test(metagenome_predictions.info2$mean.Function.Alpha.div)
bptest(mod3)
plot(mod3)
summary(mod3)
# Is endophyte diversity driven by any climatic data? NO
mod3.mLat<-update(mod3,.~. - Lat)
anova(mod3,mod3.mLat)

mod3.mLong<-update(mod3.mLat,.~. - Long)
anova(mod3.mLat,mod3.mLong)

# Annual precipitation is significant! 
mod3.mAnnualPrecipitation<-update(mod3.mLong,.~. - AnnualPrecipitation)
anova(mod3.mLong,mod3.mAnnualPrecipitation)

mod3.mAnnualMeanTemperature<-update(mod3.mLong,.~. - AnnualMeanTemperature)
anova(mod3.mLong,mod1.mAnnualMeanTemperature)

mod3.mPrecipitationSeasonality<-update(mod3.mAnnualMeanTemperature,.~. - PrecipitationSeasonality)
anova(mod3.mAnnualMeanTemperature, mod3.mPrecipitationSeasonality)

# Temperature seasonality is significant!
mod3.mTemperatureSeasonality<-update(mod3.mPrecipitationSeasonality,.~. - TemperatureSeasonality)
anova(mod3.mPrecipitationSeasonality,mod3.mTemperatureSeasonality)

mod3.mMeanDiurnalTemperatureRange<-update(mod3.mTemperatureSeasonality,.~. - MeanDiurnalTemperatureRange)
anova(mod3.mTemperatureSeasonality, mod3.mMeanDiurnalTemperatureRange)

# Min temperature is a SLIGHTLY significant predictor!
mod3.mMinTemperature<-update(mod3.mMeanDiurnalTemperatureRange,.~. - MinTemperature)
anova(mod3.mMeanDiurnalTemperatureRange, mod3.mMinTemperature)


mod3.mMaxTemperature<-update(mod3.mMinTemperature,.~. - MaxTemperature)
anova(mod3.mMinTemperature, mod3.mMaxTemperature)

mod4<- lm(Function.Alpha.div~Species,data=metagenome_predictions.info)
summary(mod4)

Composition model

X<-metagenome_predictions.info[,c(1,3)]
metagenome_predictions2<-metagenome_predictions
metagenome_predictions2$SampleID<-row.names(metagenome_predictions)
metagenome_predictions2<-merge(metagenome_predictions2,X, by="SampleID")
metagenome_predictions2$Species<-as.factor(metagenome_predictions2$Species)

metagenome_predictions2<-aggregate(metagenome_predictions2, by = list(species=metagenome_predictions2$Species), FUN=mean)
metagenome_predictions2$SampleID<-NULL

row.names(metagenome_predictions2)<-metagenome_predictions2$species
metagenome_predictions2$Species<-NULL
metagenome_predictions2$species<-NULL
sum(is.na(metagenome_predictions2))

metagenome_predictions2matched<-metagenome_predictions2[match(rownames(dist_matrix), rownames(metagenome_predictions2)), ]
beta_divMat<- vegdist(metagenome_predictions2matched, "bray",diag=T)


# Are the endophytes more similar in species that are more closely related phylogenically? NO! 
mantel(beta_divMat,dist_matrix)

# are similarity in the endophyte composition driven by climate data?


Mydata$Species<-plyr::revalue(Mydata$Species, c("Brassica_napus"="Brassica napus", "Brassica_oleracea"="Brassica oleracea","Cratoneuron"="Cratoneuron filicinum","Grimmia"="Grimmia pilifera","Microdesmis"="Microdesmis caseariifolia","Orchid"="Phalaenopsis","Oryza"="Oryza sativa","Paeonia_rockii"="Paeonia rockii","Paeonia_suffruticosa"="Paeonia suffruticosa","Pinus"="Pinus pinaster","Pinus_flexilis"="Pinus flexilis","Pylaisiella"="Pylaisia polyantha","Rothmannia"="Rothmannia macrophylla","Santiria"="Santiria apiculata","Solanum"="Solanum lycopersicum","Vitis"="Vitis vinifera","Zea"="Zea mays"))
rownames(Mydata)<-Mydata$Species


metagenome_predictions2$species<-rownames(metagenome_predictions2)
metagenome_predictions2$species<-as.factor(metagenome_predictions2$species)
metagenome_predictions2$species<-plyr::revalue(metagenome_predictions2$species, c("Brassica_napus"="Brassica napus", "Brassica_oleracea"="Brassica oleracea","Cratoneuron"="Cratoneuron filicinum","Grimmia"="Grimmia pilifera","Microdesmis"="Microdesmis caseariifolia","Orchid"="Phalaenopsis","Oryza"="Oryza sativa","Paeonia_rockii"="Paeonia rockii","Paeonia_suffruticosa"="Paeonia suffruticosa","Pinus"="Pinus pinaster","Pinus_flexilis"="Pinus flexilis","Pylaisiella"="Pylaisia polyantha","Rothmannia"="Rothmannia macrophylla","Santiria"="Santiria apiculata","Solanum"="Solanum lycopersicum","Vitis"="Vitis vinifera","Zea"="Zea mays"))
rownames(metagenome_predictions2)<-metagenome_predictions2$species
metagenome_predictions2$species<-NULL


metagenome_predictions2.hell <- decostand(metagenome_predictions2, "hell")


Mydata<-Mydata[match(rownames(metagenome_predictions2), rownames(Mydata)), ]
var <-Mydata[,c(4:15)]

formulaRDA<- rda(metagenome_predictions2.hell ~ Lat   + PrecipitationSeasonality + TemperatureSeasonality + MinTemperature + MaxTemperature+ MaxPrecipitation + ColdestPrecipitation, data=var, scale=F)

head(summary(formulaRDA))
RsquareAdj(formulaRDA)
anova(formulaRDA, permutations=1000)
anovaMARGIN<-anova.cca(formulaRDA, by="margin", permutations=1000)



varpart(metagenome_predictions2.hell, ~Lat + Long, ~ AnnualMeanTemperature + MeanDiurnalTemperatureRange + TemperatureSeasonality + MaxTemperature + MinTemperature, ~ AnnualPrecipitation + PrecipitationSeasonality + MaxPrecipitation+ MinPrecipitation + ColdestPrecipitation, data=var)

FIGURES

ggsave("nmds.FUN_PLOTFAR.pdf",FUNFAR.nmds.gg1)
Saving 7 x 7 in image
---
title: "Working with the data"
output: 
  html_notebook: 
    highlight: tango
    theme: cerulean
    toc: yes
---
#Housekeeping

```{r Load Packages, include=FALSE}
library(readr)
library(tidyverse)
library(lme4)
library(MASS)
library(MuMIn)
library(vegan)
library(fitdistrplus)
library(car)
library(bbmle)
library(lattice)
library(permute)
library(ade4)
library(lmtest)
library(phyloseq)
library(RAM)
library("BiodiversityR")
```

```{r Housekeeping, include=FALSE}
# Simplified, clean plotting theme
theme_katherine <- function () {
   theme_bw()+
   theme(legend.title=element_blank(),
         plot.title = element_text(hjust = 0.5,face="bold", size = 20), 
         panel.grid.major = element_blank(), 
         panel.grid.minor = element_blank(),
         panel.background = element_blank(),
         axis.line = element_line(colour = "black"),
         axis.title.x = element_text(size = 16),
         axis.title.y = element_text(size = 16),
         axis.text.y = element_text(size = 10),
         panel.border = element_rect(colour = "black"),
         strip.background = element_rect(colour = "black",fill="white"),
         strip.text.x = element_text(size = 12))
}

overdisp_fun <- function(model) {
  ## number of variance parameters in an n-by-n variance-covariance matrix
  vpars <- function(m) {
    nrow(m) * (nrow(m) + 1)/2
  }
  # The next two lines calculate the residual degrees of freedom
  model.df <- sum(sapply(VarCorr(model), vpars)) + length(fixef(model))
  rdf <- nrow(model.frame(model)) - model.df
  # extracts the Pearson residuals
  rp <- residuals(model, type = "pearson")
  Pearson.chisq <- sum(rp^2)
  prat <- Pearson.chisq/rdf
  # Generates a p-value. If less than 0.05, the data are overdispersed.
  pval <- pchisq(Pearson.chisq, df = rdf, lower.tail = FALSE)
  c(chisq = Pearson.chisq, ratio = prat, rdf = rdf, p = pval)
}

residfit <- function (model, col="black") {
  f<-fitted(model)
  r<-resid(model, type='pearson')
  plot(f, r, col=col, main='Residuals vs. Fitted')
  L1<-loess(r~f)
  fvec = seq(min(f),max(f),length.out=1000)
  lines(fvec,predict(L1,fvec),col='red')
}

scaleloc <- function(model,col="black") {
  f <- fitted(model)
  r <- sqrt(abs(residuals(model, type='pearson'))) #transformed pearson residuals
  plot(f,r,col=col, main='Scale-Location Plot') 
  L1 <- loess(r~f)
  fvec = seq(min(f),max(f),length.out=1000)
  lines(fvec,predict(L1,fvec),col=2)
}
```

# Loading in data

```{r Loading data, include=FALSE}
BioDatFull2 <- read.csv("~/Queens/Classes/Semester_4/Bio812_bioinf/Endophyte_project/Endophyte_project/Data/BioDatFull2.csv")
colnames(BioDatFull2)[colnames(BioDatFull2)=="Plant"] <- "Species"

mapping_file <- read.csv("~/Queens/Classes/Semester_4/Bio812_bioinf/Endophyte_project/Endophyte_project/Data/mapping_file.csv")
mapping_file$SampleID<-as.character(mapping_file$SampleID)
mapping_file$Species<-as.character(mapping_file$Species)

metagenome_predictions <- read.delim("~/Queens/Classes/Semester_4/Bio812_bioinf/Endophyte_project/Endophyte_project/Data/metagenome_predictions.txt")
head(metagenome_predictions)

ny <- as.character(metagenome_predictions$KEGG_Description)
OTU<-metagenome_predictions$OTU.ID
metagenome_predictions<-metagenome_predictions[,c(-1,-length(metagenome_predictions))]
Samples <- colnames(metagenome_predictions)
metagenome_predictions<-data.frame(t(metagenome_predictions))
colnames(metagenome_predictions) <- ny

OTU_table_Q1 <- read.delim("~/Queens/Classes/Semester_4/Bio812_bioinf/Endophyte_project/Endophyte_project/Data/97_OTU_table_QIIME1.txt", header=T)
head(OTU_table_Q1)
X<-OTU_table_Q1[,c(1,length(OTU_table_Q1))]

OTU_table<-read.delim("~/Queens/Classes/Semester_4/Bio812_bioinf/Endophyte_project/Endophyte_project/Data/97_OTU.txt")
head(OTU_table)
colnames(OTU_table)[colnames(OTU_table)=="OTU.ID"] <- "OTU_ID"
OTU_table<-merge(OTU_table,X, by="OTU_ID")
OTU_table$taxonomy.x<-NULL
colnames(OTU_table)[colnames(OTU_table)=="taxonomy.y"] <- "taxonomy"

dist_matrix <- read.csv("~/Queens/Classes/Semester_4/Bio812_bioinf/Endophyte_project/Endophyte_project/Data/dist_matrix_samples.csv", row.names=1)
```

# OTU
## ANALYSIS
### OTU diversity and such
```{r}
OTU_table.t<-transpose.OTU(OTU_table)
ab<-table(unlist(OTU_table.t))
ab[1:20] 

barplot(log(ab), las=1, xlab = "Abundand in matrix", 
        ylab = "log (frequency)", 
        ylim = c(0, max(log(ab))+2))


row.names(OTU_table)<-OTU_table$OTU_ID
OTU_table.OTU_ID<-OTU_table$OTU_ID
OTU_table$OTU_ID<-NULL
OTU_table<-OTU_table[,colSums(OTU_table[,c(1:(length(colnames(OTU_table))-1))])!=0]
OTU_table.t<-transpose.OTU(OTU_table)


OTU_table.ns<-OTU_table[rowSums(OTU_table[,c(1:(length(OTU_table)-1))])!=1,]
dim(OTU_table)
dim(OTU_table.ns)


OTU_table<-OTU_table.ns
OTU_table.t<-transpose.OTU(OTU_table)

# Seperate taxonomy
OTU_table$taxonomy<-as.character(OTU_table$taxonomy)

tax.classes <- c("kingdom", "phylum", "class", "order", "family", "genus", "species")
OTU_table.tax <- col.splitup(OTU_table, col="taxonomy", sep="; ",drop=TRUE, names=tax.classes)
OTU_table.tax$taxonomy<-OTU_table$taxonomy

```

### Making the metadata
```{r}
Samples<-colnames(OTU_table)

OTU.info<-data.frame(SampleID=Samples, Alpha.div=rep(NA, length(Samples)),Species=rep(NA, length(Samples)))
OTU.info$SampleID<-as.character(OTU.info$SampleID)

for(i in 1:(length(Samples)-1)){
OTU.info$SampleID[i]<-Samples[i]
OTU.info$Alpha.div[i]<-diversity(OTU_table.t[rownames(OTU_table.t)==Samples[i],], index = "shannon", MARGIN = 1, base = exp(1))
OTU.info$Species[i]<-mapping_file[mapping_file$SampleID==Samples[i],]$Species
}

OTU.info<- OTU.info[-nrow(OTU.info),]
OTU.info$Species<-as.factor(OTU.info$Species) 
detach(package:plyr)
OTU.info %>% group_by(Species) %>% summarise(mean.Alpha.div=mean(Alpha.div)) -> OTU.info2
OTU.info2<-merge(OTU.info2, BioDatFull2, by="Species")
OTU.info2$Species<-as.factor(OTU.info2$Species)
OTU.info$Species<-as.factor(OTU.info$Species)

```

### Diversity Model
```{r Richness model}
# Normal?
colnames(OTU.info2)
colnames(OTU.info2)[colnames(OTU.info2)=="Annual.mean.temperature...C."] <- "AnnualMeanTemperature"
colnames(OTU.info2)[colnames(OTU.info2)=="Mean.diurnal.temperature.range..mean.period.max.min.....C."] <- "MeanDiurnalTemperatureRange"
colnames(OTU.info2)[colnames(OTU.info2)=="Temperature.seasonality..C.of.V."] <- "TemperatureSeasonality"
colnames(OTU.info2)[colnames(OTU.info2)=="Max.temperature.of.warmest.week...C."] <- "MaxTemperature"
colnames(OTU.info2)[colnames(OTU.info2)=="Min.temperature.of.coldest.week...C."] <- "MinTemperature"
colnames(OTU.info2)[colnames(OTU.info2)=="Annual.precipitation..mm."] <- "AnnualPrecipitation"
colnames(OTU.info2)[colnames(OTU.info2)=="Precipitation.of.wettest.week..mm."] <- "MaxPrecipitation"
colnames(OTU.info2)[colnames(OTU.info2)=="Precipitation.of.driest.quarter..mm."] <- "MinPrecipitation"
colnames(OTU.info2)[colnames(OTU.info2)=="Precipitation.of.coldest.quarter..mm."] <- "ColdestPrecipitation"
colnames(OTU.info2)[colnames(OTU.info2)=="Precipitation.seasonality..C.of.V."] <- "PrecipitationSeasonality"


Mydata<-OTU.info2[,c(1:6,8:10,16:17,19,21,23)]
Y<-Mydata[,c(8:length(Mydata))]
covar_cor = cor(Y)
corrplot.mixed(covar_cor, upper = "ellipse", lower = "number")
Mydata_PCA = dudi.pca(df = Y, scannf = FALSE, nf = 2, center = TRUE, scale = TRUE)
Mydata_PCA$co
s.corcircle(Mydata_PCA$co)
Mydata_PCA$eig/sum(Mydata_PCA$eig)
colnames(Mydata)

Mydata$Lat <- scale(Mydata$Lat)
Mydata$Long <- scale(Mydata$Long)
Mydata$AnnualPrecipitation <- scale(Mydata$AnnualPrecipitation)
Mydata$AnnualMeanTemperature <- scale(Mydata$AnnualMeanTemperature)
Mydata$PrecipitationSeasonality <- scale(Mydata$PrecipitationSeasonality)
Mydata$TemperatureSeasonality  <- scale(Mydata$TemperatureSeasonality)
Mydata$MeanDiurnalTemperatureRange<- scale(Mydata$MeanDiurnalTemperatureRange)
Mydata$MinTemperature <- scale(Mydata$MinTemperature)
Mydata$MaxTemperature <- scale(Mydata$MaxTemperature)



mod1<-lm(mean.Alpha.div ~ Lat + Long + AnnualPrecipitation + AnnualMeanTemperature + PrecipitationSeasonality + TemperatureSeasonality  + MeanDiurnalTemperatureRange+ MinTemperature + MaxTemperature , data = Mydata)

car::qqp(OTU.info2$mean.Alpha.div, "norm")
plot(density(OTU.info2$mean.Alpha.div))
shapiro.test(OTU.info2$mean.Alpha.div)
bptest(mod1)
plot(mod1)

# Is endophyte diversity driven by any climatic data? NO
mod1.mLat<-update(mod1,.~. - Lat)
anova(mod1,mod1.mLat)

mod1.mLong<-update(mod1.mLat,.~. - Long)
anova(mod1.mLat,mod1.mLong)

mod1.mAnnualPrecipitation<-update(mod1.mLong,.~. - AnnualPrecipitation)
anova(mod1.mLong,mod1.mAnnualPrecipitation)

mod1.mAnnualMeanTemperature<-update(mod1.mAnnualPrecipitation,.~. - AnnualMeanTemperature)
anova(mod1.mAnnualPrecipitation,mod1.mAnnualMeanTemperature)

mod1.mPrecipitationSeasonality<-update(mod1.mAnnualMeanTemperature,.~. - PrecipitationSeasonality)
anova(mod1.mAnnualMeanTemperature, mod1.mPrecipitationSeasonality)

mod1.mTemperatureSeasonality<-update(mod1.mPrecipitationSeasonality,.~. - TemperatureSeasonality)
anova(mod1.mPrecipitationSeasonality,mod1.mTemperatureSeasonality)

mod1.mMeanDiurnalTemperatureRange<-update(mod1.mTemperatureSeasonality,.~. - MeanDiurnalTemperatureRange)
anova(mod1.mTemperatureSeasonality, mod1.mMeanDiurnalTemperatureRange)

# Min temperature is a significant predictor!
mod1.mMinTemperature<-update(mod1.mMeanDiurnalTemperatureRange,.~. - MinTemperature)
anova(mod1.mMeanDiurnalTemperatureRange, mod1.mMinTemperature)

# Max temperature is a significant predictor!
mod1.mMaxTemperature<-update(mod1.mMinTemperature,.~. - MaxTemperature)
anova(mod1.mMinTemperature, mod1.mMaxTemperature)


mod2<- lm(Alpha.div~Species,data=OTU.info)
summary(mod2)
```
### Composition model
```{r multivariate model}

X<-OTU.info[,c(1,3)]
OTU_table.t$SampleID<-row.names(OTU_table.t)
OTU_table.t1<-merge(OTU_table.t,X, by="SampleID")

OTU_table.t1<-aggregate(OTU_table.t1, by = list(species=OTU_table.t1$Species), FUN=mean)
OTU_table.t1$SampleID<-NULL

row.names(OTU_table.t1)<-OTU_table.t1$species
OTU_table.t1$species<-NULL
OTU_table.t1$Species<-NULL
sum(is.na(OTU_table.t1))

OTU_table.tmatched<-OTU_table.t1[match(rownames(dist_matrix), rownames(OTU_table.t1)), ]
beta_divMat<- vegdist(OTU_table.tmatched, "bray",diag=T)


# Are the endophytes more similar in species that are more closely related phylogenically? NO! 
mantel(beta_divMat,dist_matrix)

# are similarity in the endophyte composition driven by climate data?


Mydata$Species<-plyr::revalue(Mydata$Species, c("Brassica_napus"="Brassica napus", "Brassica_oleracea"="Brassica oleracea","Cratoneuron"="Cratoneuron filicinum","Grimmia"="Grimmia pilifera","Microdesmis"="Microdesmis caseariifolia","Orchid"="Phalaenopsis","Oryza"="Oryza sativa","Paeonia_rockii"="Paeonia rockii","Paeonia_suffruticosa"="Paeonia suffruticosa","Pinus"="Pinus pinaster","Pinus_flexilis"="Pinus flexilis","Pylaisiella"="Pylaisia polyantha","Rothmannia"="Rothmannia macrophylla","Santiria"="Santiria apiculata","Solanum"="Solanum lycopersicum","Vitis"="Vitis vinifera","Zea"="Zea mays"))
rownames(Mydata)<-Mydata$Species


OTU_table.t1$species<-rownames(OTU_table.t1)
OTU_table.t1$species<-as.factor(OTU_table.t1$species)
OTU_table.t1$species<-plyr::revalue(OTU_table.t1$species, c("Brassica_napus"="Brassica napus", "Brassica_oleracea"="Brassica oleracea","Cratoneuron"="Cratoneuron filicinum","Grimmia"="Grimmia pilifera","Microdesmis"="Microdesmis caseariifolia","Orchid"="Phalaenopsis","Oryza"="Oryza sativa","Paeonia_rockii"="Paeonia rockii","Paeonia_suffruticosa"="Paeonia suffruticosa","Pinus"="Pinus pinaster","Pinus_flexilis"="Pinus flexilis","Pylaisiella"="Pylaisia polyantha","Rothmannia"="Rothmannia macrophylla","Santiria"="Santiria apiculata","Solanum"="Solanum lycopersicum","Vitis"="Vitis vinifera","Zea"="Zea mays"))
rownames(OTU_table.t1)<-OTU_table.t1$species
OTU_table.t1$species<-NULL


OTU_table.t1.hell <- decostand(OTU_table.t1, "hell")


Mydata<-Mydata[match(rownames(OTU_table.t1), rownames(Mydata)), ]
var <-Mydata[,c(3:16)]

formulaRDA<- rda(OTU_table.t1.hell ~ Lat + Long + AnnualPrecipitation + AnnualMeanTemperature + PrecipitationSeasonality + TemperatureSeasonality + MinTemperature + MaxTemperature + MeanDiurnalTemperatureRange+ MinPrecipitation+ MaxPrecipitation + ColdestPrecipitation, data=var, scale=F)

head(summary(formulaRDA))
RsquareAdj(formulaRDA)
anova(formulaRDA, permutations=1000)
anovaMARGIN<-anova.cca(formulaRDA, by="margin", permutations=1000)

varpart(OTU_table.t1.hell, ~Lat + Long, ~ AnnualMeanTemperature + MeanDiurnalTemperatureRange + TemperatureSeasonality + MaxTemperature + MinTemperature, ~ AnnualPrecipitation + PrecipitationSeasonality + MaxPrecipitation+ MinPrecipitation + ColdestPrecipitation, data=var)
```

## FIGURES

```{r}
OTU.info$Species<-plyr::revalue(OTU.info$Species, c("Brassica_napus"="Brassica napus", "Brassica_oleracea"="Brassica oleracea","Cratoneuron"="Cratoneuron filicinum","Grimmia"="Grimmia pilifera","Microdesmis"="Microdesmis caseariifolia","Orchid"="Phalaenopsis","Oryza"="Oryza sativa","Paeonia_rockii"="Paeonia rockii","Paeonia_suffruticosa"="Paeonia suffruticosa","Pinus"="Pinus pinaster","Pinus_flexilis"="Pinus flexilis","Pylaisiella"="Pylaisia polyantha","Rothmannia"="Rothmannia macrophylla","Santiria"="Santiria apiculata","Solanum"="Solanum lycopersicum","Vitis"="Vitis vinifera","Zea"="Zea mays"))


# Alpha to secies
Adiv_species_OTU<-ggplot(OTU.info, aes(y=Alpha.div,x=Species))+
  geom_boxplot(aes(color=Species))+
  labs(y="Shannon diversity")+
  theme_katherine()+ theme(axis.text.x = element_text(angle = 90, hjust = 1),legend.position="none")+
  ggtitle("Alpha Diversity of OTUs by Species")
ggsave("Adiv_species.OTU_PLOT.pdf",Adiv_species_OTU)

# RDA
smry <- summary(formulaRDA)
df1  <- data.frame(smry$sites[,1:2])       # PC1 and PC2
df2  <- data.frame(smry$biplot[,1:2])     # loadings for PC1 and PC2
rda.plot <- ggplot(df1, aes(x=RDA1, y=RDA2)) + 
  geom_text(aes(label=rownames(df1)),size=3,position=position_jitter(width=.2,height=.2)) +
  geom_point(aes(alpha=0.3)) +
  geom_hline(yintercept=0, linetype="dotted") +
  geom_vline(xintercept=0, linetype="dotted")  

names<-c("Latitude","Longitude","\nMean Annual Temp", "Diurnal Temp Range", "\nTemp Seasonality", "Max Temp\n", "Min Temp", "Annual Precipitation\n", "Max Precipitation", "Precipitation Seasonality", "Min Precipitation", "Coldest Precipitation    ")
rownames(df2)
(formulaRDA.PLOT<-rda.plot +
  geom_segment(data=df2, aes(x=0, xend=RDA1, y=0, yend=RDA2), 
               color="red", arrow=arrow(length=unit(0.01,"npc"))) +
  geom_text(data=df2, 
            aes(x=RDA1,y=RDA2,label=names,
                hjust=0.5*(1-sign(RDA1)),vjust=0.5*(1-sign(RDA2))), 
            color="red", size=4)+
  coord_cartesian(ylim = c(-0.8, 0.8),xlim = c(-1.3, 1.3)) +theme_katherine()+ theme(legend.position="none"))
ggsave("formulaRDA.OTU_PLOT.pdf",formulaRDA.PLOT)

# NMDS
#Make a matrix with no row or column equal to 0 (do not enclude the env variable (GM COVERAGE))
OTU_table.t2<-merge(OTU_table.t,X, by="SampleID")
OTU_table.t2$SampleID<-NULL
rownames.M<-OTU_table.t2$Species
OTU_table.t2$Species<-NULL
M <- as.matrix(OTU_table.t2)
M[is.na(M)] <- 0
which( colSums(M)==0 )
which( rowSums(M)==0 )

rownames(M) <- rownames.M

dist_M <- vegdist(M, method = "bray", binary = T)
#The metaMDS analysis could have done the distance matrix internally but i would rather control it since i have presence/abscence
meta.nmds <- metaMDS(dist_M, k=2, trymax = 2000)
str(meta.nmds)
stressplot(meta.nmds)

Species<-data.frame(Species=as.factor(rownames.M))
# envfit
envfit <- envfit(meta.nmds, env = Species, perm = 999) #standard envfit
envfit

#data for plotting 
##NMDS points
NMDS.data<-Species 
NMDS.data$NMDS1<-meta.nmds$points[ ,1] 
NMDS.data$NMDS2<-meta.nmds$points[ ,2] 
colnames(NMDS.data)[1] <- "Species"

mult <- 1 #multiplier for the arrows and text for envfit below. You can change this and then rerun the plot command.
library(ggplot2)
(OTU.nmds.gg1 <- ggplot(data = NMDS.data, aes(y = NMDS2, x = NMDS1))+ 
    geom_point( aes(color = NMDS.data$Species), size = 1.5,alpha=0.6)+ 
    coord_cartesian(xlim = c(-0.25,0.25),ylim = c(-0.1,0.1)) + 
    theme_katherine())
ggsave("nmds.OTU_PLOT.pdf",OTU.nmds.gg1)
```


# Functions
## ANALYSIS
### Functional diversity and such
```{r}
metagenome_predictions

metagenome_predictions<-metagenome_predictions[,colSums(metagenome_predictions[,])!=0]
metagenome_predictions<-metagenome_predictions[rowSums(metagenome_predictions[,])!=0,]
metagenome_predictions.ns<-metagenome_predictions[,colSums(metagenome_predictions[,])!=1]
dim(metagenome_predictions)
dim(metagenome_predictions.ns)

metagenome_predictions<-metagenome_predictions.ns

Functions<-colnames(metagenome_predictions)
Samples<-rownames(metagenome_predictions)

metagenome_predictions.info<-data.frame(SampleID=Samples, OTU.Alpha.div=rep(NA, length(Samples)),Species=rep(NA, length(Samples)), Function.Alpha.div=rep(NA, length(Samples)))

which(sapply(OTU_table.t, is.character))
OTU_table.t$SampleID<-NULL
for(i in 1:(length(Samples))){
metagenome_predictions.info$SampleID[i]<-Samples[i]
metagenome_predictions.info$OTU.Alpha.div[i]<-diversity(OTU_table.t[rownames(OTU_table.t)==Samples[i],], index = "shannon", MARGIN = 1, base = exp(1))
metagenome_predictions.info$Function.Alpha.div[i]<-diversity(metagenome_predictions[rownames(metagenome_predictions)==Samples[i],], index = "shannon", MARGIN = 1, base = exp(1))
metagenome_predictions.info$Species[i]<-mapping_file[mapping_file$SampleID==Samples[i],]$Species
}


metagenome_predictions.info$Species<-as.factor(metagenome_predictions.info$Species)

metagenome_predictions.info %>% dplyr::group_by(Species) %>% summarise(mean.OTU.Alpha.div=mean(OTU.Alpha.div),mean.Function.Alpha.div=mean(Function.Alpha.div)) -> metagenome_predictions.info2
metagenome_predictions.info2<-merge(metagenome_predictions.info2, BioDatFull2, by="Species")

```

### Functional diversity model
```{r Richness model FUN}
# Normal?
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Annual.mean.temperature...C."] <- "AnnualMeanTemperature"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Mean.diurnal.temperature.range..mean.period.max.min.....C."] <- "MeanDiurnalTemperatureRange"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Temperature.seasonality..C.of.V."] <- "TemperatureSeasonality"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Max.temperature.of.warmest.week...C."] <- "MaxTemperature"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Min.temperature.of.coldest.week...C."] <- "MinTemperature"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Annual.precipitation..mm."] <- "AnnualPrecipitation"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Precipitation.of.wettest.week..mm."] <- "MaxPrecipitation"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Precipitation.of.driest.quarter..mm."] <- "MinPrecipitation"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Precipitation.of.coldest.quarter..mm."] <- "ColdestPrecipitation"
colnames(metagenome_predictions.info2)[colnames(metagenome_predictions.info2)=="Precipitation.seasonality..C.of.V."] <- "PrecipitationSeasonality"


Mydata<-metagenome_predictions.info2[,c(1:7,9:11,17,18,20,22,24)]
colnames(metagenome_predictions.info2)
Mydata$Lat <- scale(Mydata$Lat)
Mydata$Long <- scale(Mydata$Long)
Mydata$AnnualPrecipitation <- scale(Mydata$AnnualPrecipitation)
Mydata$AnnualMeanTemperature <- scale(Mydata$AnnualMeanTemperature)
Mydata$PrecipitationSeasonality <- scale(Mydata$PrecipitationSeasonality)
Mydata$TemperatureSeasonality  <- scale(Mydata$TemperatureSeasonality)
Mydata$MeanDiurnalTemperatureRange<- scale(Mydata$MeanDiurnalTemperatureRange)
Mydata$MinTemperature <- scale(Mydata$MinTemperature)
Mydata$MaxTemperature <- scale(Mydata$MaxTemperature)



mod3<-lm(mean.Function.Alpha.div ~ Lat + Long + AnnualPrecipitation + AnnualMeanTemperature + PrecipitationSeasonality + TemperatureSeasonality  + MeanDiurnalTemperatureRange+ MinTemperature + MaxTemperature , data = Mydata)

car::qqp(metagenome_predictions.info2$mean.Function.Alpha.div, "norm")
plot(density(metagenome_predictions.info2$mean.Function.Alpha.div))
shapiro.test(metagenome_predictions.info2$mean.Function.Alpha.div)
bptest(mod3)
plot(mod3)
summary(mod3)
# Is endophyte diversity driven by any climatic data? NO
mod3.mLat<-update(mod3,.~. - Lat)
anova(mod3,mod3.mLat)

mod3.mLong<-update(mod3.mLat,.~. - Long)
anova(mod3.mLat,mod3.mLong)

# Annual precipitation is significant! 
mod3.mAnnualPrecipitation<-update(mod3.mLong,.~. - AnnualPrecipitation)
anova(mod3.mLong,mod3.mAnnualPrecipitation)

mod3.mAnnualMeanTemperature<-update(mod3.mLong,.~. - AnnualMeanTemperature)
anova(mod3.mLong,mod1.mAnnualMeanTemperature)

mod3.mPrecipitationSeasonality<-update(mod3.mAnnualMeanTemperature,.~. - PrecipitationSeasonality)
anova(mod3.mAnnualMeanTemperature, mod3.mPrecipitationSeasonality)

# Temperature seasonality is significant!
mod3.mTemperatureSeasonality<-update(mod3.mPrecipitationSeasonality,.~. - TemperatureSeasonality)
anova(mod3.mPrecipitationSeasonality,mod3.mTemperatureSeasonality)

mod3.mMeanDiurnalTemperatureRange<-update(mod3.mTemperatureSeasonality,.~. - MeanDiurnalTemperatureRange)
anova(mod3.mTemperatureSeasonality, mod3.mMeanDiurnalTemperatureRange)

# Min temperature is a SLIGHTLY significant predictor!
mod3.mMinTemperature<-update(mod3.mMeanDiurnalTemperatureRange,.~. - MinTemperature)
anova(mod3.mMeanDiurnalTemperatureRange, mod3.mMinTemperature)


mod3.mMaxTemperature<-update(mod3.mMinTemperature,.~. - MaxTemperature)
anova(mod3.mMinTemperature, mod3.mMaxTemperature)

mod4<- lm(Function.Alpha.div~Species,data=metagenome_predictions.info)
summary(mod4)
```


### Composition model
```{r multivariate model FUN}
X<-metagenome_predictions.info[,c(1,3)]
metagenome_predictions2<-metagenome_predictions
metagenome_predictions2$SampleID<-row.names(metagenome_predictions)
metagenome_predictions2<-merge(metagenome_predictions2,X, by="SampleID")
metagenome_predictions2$Species<-as.factor(metagenome_predictions2$Species)

metagenome_predictions2<-aggregate(metagenome_predictions2, by = list(species=metagenome_predictions2$Species), FUN=mean)
metagenome_predictions2$SampleID<-NULL

row.names(metagenome_predictions2)<-metagenome_predictions2$species
metagenome_predictions2$Species<-NULL
metagenome_predictions2$species<-NULL
sum(is.na(metagenome_predictions2))

metagenome_predictions2matched<-metagenome_predictions2[match(rownames(dist_matrix), rownames(metagenome_predictions2)), ]
beta_divMat<- vegdist(metagenome_predictions2matched, "bray",diag=T)


# Are the endophytes more similar in species that are more closely related phylogenically? NO! 
mantel(beta_divMat,dist_matrix)

# are similarity in the endophyte composition driven by climate data?


Mydata$Species<-plyr::revalue(Mydata$Species, c("Brassica_napus"="Brassica napus", "Brassica_oleracea"="Brassica oleracea","Cratoneuron"="Cratoneuron filicinum","Grimmia"="Grimmia pilifera","Microdesmis"="Microdesmis caseariifolia","Orchid"="Phalaenopsis","Oryza"="Oryza sativa","Paeonia_rockii"="Paeonia rockii","Paeonia_suffruticosa"="Paeonia suffruticosa","Pinus"="Pinus pinaster","Pinus_flexilis"="Pinus flexilis","Pylaisiella"="Pylaisia polyantha","Rothmannia"="Rothmannia macrophylla","Santiria"="Santiria apiculata","Solanum"="Solanum lycopersicum","Vitis"="Vitis vinifera","Zea"="Zea mays"))
rownames(Mydata)<-Mydata$Species


metagenome_predictions2$species<-rownames(metagenome_predictions2)
metagenome_predictions2$species<-as.factor(metagenome_predictions2$species)
metagenome_predictions2$species<-plyr::revalue(metagenome_predictions2$species, c("Brassica_napus"="Brassica napus", "Brassica_oleracea"="Brassica oleracea","Cratoneuron"="Cratoneuron filicinum","Grimmia"="Grimmia pilifera","Microdesmis"="Microdesmis caseariifolia","Orchid"="Phalaenopsis","Oryza"="Oryza sativa","Paeonia_rockii"="Paeonia rockii","Paeonia_suffruticosa"="Paeonia suffruticosa","Pinus"="Pinus pinaster","Pinus_flexilis"="Pinus flexilis","Pylaisiella"="Pylaisia polyantha","Rothmannia"="Rothmannia macrophylla","Santiria"="Santiria apiculata","Solanum"="Solanum lycopersicum","Vitis"="Vitis vinifera","Zea"="Zea mays"))
rownames(metagenome_predictions2)<-metagenome_predictions2$species
metagenome_predictions2$species<-NULL


metagenome_predictions2.hell <- decostand(metagenome_predictions2, "hell")


Mydata<-Mydata[match(rownames(metagenome_predictions2), rownames(Mydata)), ]
var <-Mydata[,c(4:15)]

formulaRDA<- rda(metagenome_predictions2.hell ~ Lat   + PrecipitationSeasonality + TemperatureSeasonality + MinTemperature + MaxTemperature+ MaxPrecipitation + ColdestPrecipitation, data=var, scale=F)

head(summary(formulaRDA))
RsquareAdj(formulaRDA)
anova(formulaRDA, permutations=1000)
anovaMARGIN<-anova.cca(formulaRDA, by="margin", permutations=1000)



varpart(metagenome_predictions2.hell, ~Lat + Long, ~ AnnualMeanTemperature + MeanDiurnalTemperatureRange + TemperatureSeasonality + MaxTemperature + MinTemperature, ~ AnnualPrecipitation + PrecipitationSeasonality + MaxPrecipitation+ MinPrecipitation + ColdestPrecipitation, data=var)
```

## FIGURES

```{r}
metagenome_predictions.info2$Species<-as.factor(metagenome_predictions.info2$Species)
metagenome_predictions.info2$Species<-plyr::revalue(metagenome_predictions.info2$Species, c("Brassica_napus"="Brassica napus", "Brassica_oleracea"="Brassica oleracea","Cratoneuron"="Cratoneuron filicinum","Grimmia"="Grimmia pilifera","Microdesmis"="Microdesmis caseariifolia","Orchid"="Phalaenopsis","Oryza"="Oryza sativa","Paeonia_rockii"="Paeonia rockii","Paeonia_suffruticosa"="Paeonia suffruticosa","Pinus"="Pinus pinaster","Pinus_flexilis"="Pinus flexilis","Pylaisiella"="Pylaisia polyantha","Rothmannia"="Rothmannia macrophylla","Santiria"="Santiria apiculata","Solanum"="Solanum lycopersicum","Vitis"="Vitis vinifera","Zea"="Zea mays"))


# Alpha to secies
Adiv_species_FUN<-ggplot(metagenome_predictions.info, aes(y=Function.Alpha.div,x=Species))+
  geom_boxplot(aes(color=Species))+
  labs(y="Shannon diversity")+
  theme_katherine()+ theme(axis.text.x = element_text(angle = 90, hjust = 1),legend.position="none")+
  ggtitle("Alpha Diversity of Functions by Species")
ggsave("Adiv_species.FUN_PLOT.pdf",Adiv_species_FUN)

# RDA
smry <- summary(formulaRDA)
df1  <- data.frame(smry$sites[,1:2])       # PC1 and PC2
df2  <- data.frame(smry$biplot[,1:2])     # loadings for PC1 and PC2
rda.plot <- ggplot(df1, aes(x=RDA1, y=RDA2)) + 
  geom_text(aes(label=rownames(df1)),size=3,position=position_jitter(width=.2,height=.2)) +
  geom_point(aes(alpha=0.3)) +
  geom_hline(yintercept=0, linetype="dotted") +
  geom_vline(xintercept=0, linetype="dotted")  
rownames(df2)
names<-c("Latitude", "Precipitation Seasonality", "Temperature Seasonality", "Min Temperature", "Max Temperature", "Max Precipitation", "Coldest Precipitation\n")

(formulaRDA.FUNPLOT<-rda.plot +
  geom_segment(data=df2, aes(x=0, xend=RDA1, y=0, yend=RDA2), 
               color="red", arrow=arrow(length=unit(0.01,"npc"))) +
  geom_text(data=df2, 
            aes(x=RDA1,y=RDA2,label=names,
                hjust=0.5*(1-sign(RDA1)),vjust=0.5*(1-sign(RDA2))), 
            color="red", size=4)+
  coord_cartesian(ylim = c(-1.1, 0.8),xlim = c(-1, 1)) +theme_katherine()+ theme(legend.position="none"))
ggsave("formulaRDA.FUN_PLOT.pdf",formulaRDA.FUNPLOT)

# NMDS
#Make a matrix with no row or column equal to 0 (do not enclude the env variable (GM COVERAGE))
metagenome_predictions$SampleID<-rownames(metagenome_predictions)
metagenome_predictions3<-merge(metagenome_predictions,X, by="SampleID")
metagenome_predictions3$SampleID<-NULL
metagenome_predictions3$Species<-as.factor(metagenome_predictions3$Species)
metagenome_predictions3$Species<-plyr::revalue(metagenome_predictions3$Species, c("Brassica_napus"="Brassica napus", "Brassica_oleracea"="Brassica oleracea","Cratoneuron"="Cratoneuron filicinum","Grimmia"="Grimmia pilifera","Microdesmis"="Microdesmis caseariifolia","Orchid"="Phalaenopsis","Oryza"="Oryza sativa","Paeonia_rockii"="Paeonia rockii","Paeonia_suffruticosa"="Paeonia suffruticosa","Pinus"="Pinus pinaster","Pinus_flexilis"="Pinus flexilis","Pylaisiella"="Pylaisia polyantha","Rothmannia"="Rothmannia macrophylla","Santiria"="Santiria apiculata","Solanum"="Solanum lycopersicum","Vitis"="Vitis vinifera","Zea"="Zea mays"))

rownames.M<-metagenome_predictions3$Species
metagenome_predictions3$Species<-NULL
metagenome_predictions3$SampleID<-NULL
M <- as.matrix(metagenome_predictions3)
M[is.na(M)] <- 0
which( colSums(M)==0 )
which( rowSums(M)==0 )

rownames(M) <- rownames.M

dist_M <- vegdist(M, method = "bray", binary = T)


meta.nmds <- metaMDS(dist_M, k=2, trymax = 3000)
str(meta.nmds)
stressplot(meta.nmds)

Species<-data.frame(Species=as.factor(rownames.M))
# envfit
envfit <- envfit(meta.nmds, env = Species, perm = 999) #standard envfit
envfit

#data for plotting 
##NMDS points
NMDS.data<-Species 
NMDS.data$NMDS1<-meta.nmds$points[ ,1] 
NMDS.data$NMDS2<-meta.nmds$points[ ,2] 
colnames(NMDS.data)[1] <- "Species"

mult <- 1 #multiplier for the arrows and text for envfit below. You can change this and then rerun the plot command.

(FUNCLOSE.nmds.gg1 <- ggplot(data = NMDS.data, aes(y = NMDS2, x = NMDS1))+ 
    geom_point( aes(color = NMDS.data$Species), size = 1.5,alpha=0.6)+ 
    coord_cartesian(xlim = c(-0.2,0.1),ylim = c(-0.07,0.025)) + 
    theme_katherine())
ggsave("nmds.FUN_PLOTCLOSE.pdf",FUNCLOSE.nmds.gg1)

(FUNFAR.nmds.gg1 <- ggplot(data = NMDS.data, aes(y = NMDS2, x = NMDS1))+ 
    geom_point( aes(color = NMDS.data$Species), size = 1.5,alpha=0.6)+ 
    theme_katherine())
ggsave("nmds.FUN_PLOTFAR.pdf",FUNFAR.nmds.gg1)
```